Mike Samboy

What’s in Steve Davis’s Agentic GTM OS? A system built around a single failure point in every company: GTM breaks at the seams. Sales closes. CS inherits. Presales translates. Context disappears. Steven has spent decades operating inside that system and rebuilding it. 7 exits. Global CRO across sales, marketing, customer success, and partners. Now CEO at Provarity, focused on the most under-architected layer in GTM: presales. His model: �� revenue is a single system, not a chain of functions – ownership spans the full lifecycle, not stages – decisions are instrumented, not inferred – presales becomes a control point for deal quality, not a support role The constraint is no longer execution. It’s coherence. Steven brings this perspective to the CEO Roundtable: CEO panel: Agentic GTM Methodology — From Experimentation to Operating Discipline Mar 26 | 10:00 am PT A discussion on how experienced operators rebuild GTM as a system—and why most implementations fail at the seams. Attend free/virtual: https://lnkd.in/dBK45pYE Gainsight Vivun Vidyard Amoeba AI 1mind EverWorker ZoomInfo Alta | AI Revenue Workforce Von Provarity RevSure AI GTM Buddy Spara Across AI Dialpad SalesboxAI Genesis Computing Nooks

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Most enterprise GTM failures aren’t execution issues. They’re design issues. In this clip, Rik Schrader explains why starting with a clear North Star - and aligning the org and selling motion to it - is what creates early traction and prevents constant GTM rebuilds. Full episode: https://lnkd.in/dYU2WawZ

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What happens when SaaS CEOs stop operating on gut feel… and start scaling with a proven GTM system? That’s the real impact of Aibiliti’s Market Entry Playbook (MEP). From cutting months off vertical launches to reducing CAC, improving sales velocity, and aligning GTM teams around one clear plan — CEOs are using MEP to turn unpredictable growth into a repeatable engine. What CEOs gain with MEP: • Faster, de-risked entry into new markets • A clear ICP → content → sales execution path • Predictable ROI across every vertical • GTM teams finally moving in one direction • A system they can run again and again If your next market move needs clarity, conviction, and measurable impact — start with a playbook built for execution, not theory. Watch the video to see how MEP is transforming real SaaS outcomes. #SaaS #GoToMarket #GrowthLeadership #B2BMarketing #MarketExpansion #Aibiliti #StartupGrowth

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The agent market is polarizing into two extremes. 🧩 1) Builders (e.g. n8n, clay and many others): flexible tooling where customers assemble bespoke “agentic” workflows. 📦 2) Packaged vertical agents: pre-built solutions that promise outcomes out of the box (security triage, sales research, IT helpdesk, finance ops). Under the hood, both share the same unit of abstraction: a workflow. The key difference is who authors it: 🧑‍💻 Builders: authored by the customer, optimized for their environment. 🧠 Packaged agents: authored by the vendor/SMEs, optimized for many environments. Each extreme breaks in predictable ways. 🧩 Builders break like code: they’re great until reality changes—new data, new edge cases, new tools, new questions. Then the workflow either fails or degrades into “automation cosplay.” Many flows never needed AI; they needed glue. 📦 Packaged agents break on variance: real organizations don’t follow the vendor’s ontology. Data is fragmented, naming is inconsistent, policies are full of exceptions, and “best practice” is aspirational. The agent is often smart but not situationally correct. So the real gap isn’t “more agents.” It’s a better unit of adoption: something that carries domain expertise and survives contact with a messy environment. The middle ground is specialized agent templates: opinionated starting points (domain language + best-practice playbooks) with a deliberate adaptation surface—connectors, schema/ontology mapping, guardrails, and human checkpoints—plus an eval harness so teams can tune behavior, version changes, and rollback safely inside an Agent Builder/IDE. 🏁 In a fragmented space, the winners won’t just sell “agents.” They’ll ship templates that become production systems—because they come with a customizer that keeps them from becoming a bespoke services project.

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Thanks to Paxton Earl, SEG Analyst and contributor to our upcoming 3Q25 Quarterly SaaS Report, for breaking down the Top 10 SaaS Deals last quarter. Q3 saw $61B in SaaS M&A, driven by the intersection of SaaS and AI. From Thoma Bravo’s $18B quarter to Blackstone’s acquisition of Enverus, buyers are targeting software platforms built on data, vertical depth, and AI capabilities. #Q3 #AI #AIInsights #SaaS

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The Agent Kit Just Made Your Entire SaaS Portfolio Obsolete. The market's reaction to the launch of "Apps in ChatGPT" and the "Agent Kit" is a textbook case of misinterpreting a paradigm shift as an incremental feature. Analysts are celebrating a new "app store," a flawed analogy that dangerously underestimates the disruption at hand. Our research indicates this isn't about adding plugins to a chatbot. This is the starting gun for the autonomous agent economy. The traditional, human-in-the-loop SaaS model is now on life support. Here’s the first-principles reality and what it means for allocators: "Apps in ChatGPT" Aren't Destinations. They Are Skills. For the last 15 years, software has been a place you go. You open an app, you click buttons, you are the processor. That era is over. These new "apps" are not destinations, they are specialized, API-callable skills for AI agents to consume. Their customer is not a human, it's another piece of software. Your UI is no longer a moat. The Agent Kit is the Factory for Autonomous Workers. The true revolution is the Agent Kit. It allows developers and businesses to build autonomous agents that can set goals, reason and then chain together multiple "apps" (skills) to execute complex workflows. Think of an agent that can conduct market research, write a strategy memo, create a marketing campaign, and deploy it, all without human intervention. The Death of "Per-Seat" Pricing. The entire valuation framework for SaaS is built on per-seat, monthly recurring revenue. This model is now fundamentally broken. Why pay for 100 human seats in a CRM when a single, autonomous agent can manage the entire sales pipeline by leveraging various "apps" for a fraction of the cost? The new model is per-task or per-API-call. This is a massively deflationary event for incumbent software giants. A Historic Reallocation of Value is Beginning. Capital must now shift from businesses that employ humans to use software to businesses that either: Build the Agents: The platforms providing the core agent kits and orchestration engines. Provide a Critical "Skill": Niche, best-in-class tools with a defensible data or model advantage that agents will pay to use (e.g., the definitive financial data API, the most accurate logistics routing tool). Power the Infrastructure: The compute, energy, and data pipeline providers that will fuel this explosion in machine-to-machine traffic. Investment Implications: We believe allocators must immediately stress-test their portfolios against this new reality. Every company predicated on charging for a human to click through a workflow is now a legacy asset. The volatility we anticipate is not risk, it is the repricing of the entire software and services sector. The key investment question is no longer "How many users does your app have?" #Tech #openai #SaaS #appsinchatgpt #chatgpt #agentkit #AI #kevariscapital #investing

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GTM Orchestration: When Teams Play the Same Song. 🧠 Here’s a pattern we’ve seen across high-performing SaaS orgs 👇 Marketing generates interest ✅ Sales creates urgency ✅ PreSales builds confidence ✅ Product anchors vision ✅ CS ensures adoption When these functions are siloed, deals get stuck in fog. When they’re orchestrated, deals glide like clockwork. One of our clients recently created a GTM “war room” where Marketing, AEs, SEs, Product & CS met every Thursday for 30 minutes. Result? ⚡ Shorter cycles, cleaner handoffs, and a 22% increase in win rate within 2 quarters. GTM isn’t about adding more steps. It’s about syncing the rhythm. 👉 How does your GTM team ensure orchestration across functions? #GTM #PreSales #EnterpriseSales #SolutionEngineering #SaaS #SalesLeadership

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The evolving B2B tech landscape makes competitive displacement the default path for GTM teams. Simultaneously, it adds new layers of organizational risk, internal scrutiny, and emotional complexity for buyers. We created The Definitive Guide for Competitive Displacement to give GTM teams a smarter way forward—rooted in precise intelligence, not spray-and-pray ABM. Inside, you’ll discover: ▪️ Why traditional competitive intelligence leaves Sales, Marketing, CS, and Product flying blind ▪️ The exact data signals that show which accounts are truly ready to switch — and which never will ▪️ A step-by-step campaign framework for turning competitor intelligence into pipeline ▪️ GTM best practices from strategy to execution ▪️ How to fuse data, content, and activation into a single displacement play The switching window opens for buyers when the cost of staying exceeds the risk of moving. This guide helps you locate that window and act before it closes:

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Traditional B2B SaaS GTM models weren’t designed for cloud marketplaces, hyperscaler partnerships, or co-sell motions. But that’s exactly where modern SaaS companies live. The biggest shift we see at Cloudable.AI is that GTM is no longer a linear funnel. It’s a partner-enabled growth engine. Let’s compare: ❌ Traditional SaaS GTM: Marketing ➝ SDR ➝ AE ➝ Deal Desk ➝ Closed Won ✅ Modern AWS-Aligned GTM: Co-Marketing ➝ AWS Field Engagement ➝ Marketplace Listing ➝ Funding ➝ Procurement Bypass ➝ Closed Won ➝ Land & Expand If your GTM still looks like the first model, you're not tapping the opportunity Amazon Web Services (AWS) brings to the table. At Cloudable.AI we help ISVs rebuild GTM for the ecosystem they’re in, not the one they left behind. DM us if you're ready to make your GTM cloud-native. #GTM #SaaS #AWSMarketplace #ISV #Cloudable #CoSell #ChannelStrategy #ModernGTM #AWSPartners #SaaSGrowth

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